52 citations · 112 across the 27 of their papers we have counts for
10 papers · 1 filter
I2EDL: Interactive Instruction Error Detection and Localization
Francesco Taioli, Stefano Rosa, Alberto Castellini +5
In the Vision-and-Language Navigation in Continuous Environments (VLN-CE) task, the human user guides an autonomous agent to reach a target goal via a series of low-level actions f…
Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation
Francesco Taioli, Stefano Rosa, Alberto Castellini +5
Vision-and-Language Navigation in Continuous Environments (VLN-CE) is one of the most intuitive yet challenging embodied AI tasks. Agents are tasked to navigate towards a target go…
Unsupervised Active Visual Search with Monte Carlo planning under Uncertain Detections
Francesco Taioli, Francesco Giuliari, Yiming Wang +6
We propose a solution for Active Visual Search of objects in an environment, whose 2D floor map is the only known information. Our solution has three key features that make it more…
Constrained Reinforcement Learning and Formal Verification for Safe Colonoscopy Navigation
Davide Corsi, Luca Marzari, Ameya Pore +4
The field of robotic Flexible Endoscopes (FEs) has progressed significantly, offering a promising solution to reduce patient discomfort. However, the limited autonomy of most robot…
Constrained Reinforcement Learning for Robotics via Scenario-Based Programming
Davide Corsi, Raz Yerushalmi, Guy Amir +3
Deep reinforcement learning (DRL) has achieved groundbreaking successes in a wide variety of robotic applications. A natural consequence is the adoption of this paradigm for safety…
Verifying Learning-Based Robotic Navigation Systems
Guy Amir, Davide Corsi, Raz Yerushalmi +4
Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for tasks where complex policies are learned within reactive systems. Unfortunately, these policies a…
Safe Reinforcement Learning using Formal Verification for Tissue Retraction in Autonomous Robotic-Assisted Surgery
Ameya Pore, Davide Corsi, Enrico Marchesini +4
Deep Reinforcement Learning (DRL) is a viable solution for automating repetitive surgical subtasks due to its ability to learn complex behaviours in a dynamic environment. This tas…
POMP++: Pomcp-based Active Visual Search in unknown indoor environments
Francesco Giuliari, Alberto Castellini, Riccardo Berra +5
In this paper we focus on the problem of learning online an optimal policy for Active Visual Search (AVS) of objects in unknown indoor environments. We propose POMP++, a planning s…
Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks
Luca Marzari, Ameya Pore, Diego Dall'Alba +3
Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the data-hungr…
POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments
Yiming Wang, Francesco Giuliari, Riccardo Berra +5
In this paper we focus on the problem of learning an optimal policy for Active Visual Search (AVS) of objects in known indoor environments with an online setup. Our POMP method use…